جستجوی مقالات مرتبط با کلیدواژه « یادگیری ماشین » در نشریات گروه « اقتصاد »

In this research, based on the historical statistics of the deposits of a private bank (Ansar bank before merging) into four different categories (funds), we are looking for the results and prediction of bank deposits in each of the categories, using machine learning prediction methods for the first time in internal studies. In this forecasting method, the superiority of the machine learning method is confirmed due to the examination of all the time changes of the data in a large volume (from the beginning until now). Forecasting the above deposit statistics for bank decision makers in the future according to the economic conditions can be the basis of macro banking decisions. The results show that partly stable long-term deposit and stable deposit will take a downward trend in the future in the Ansar bank, and unstable and interest-sensitive deposit will also take a downward trend. Qarzol-ha-sa-ne deposit has an upward trend, although it has always fluctuations similar to sin-cos fluctuations. It should be noted that this machine learning prediction method is a stable method without sensitivity analysis and has high reliability. According to this research, paying attention to long-term and stable deposits as well as Qarzol-ha-sa-ne is of great importance in the bank’s budget.

Machine learning is a subfield of artificial intelligence (AI). The goal of machine learning in general is to understand the structure of data and fit that data into models that people can understand and use. Today, every technology user has benefited from machine learning. Facial recognition technology allows social media platforms to help users tag and share photos of their friends. Optical Character Recognition (OCR) technology converts text images into animated type. Recommendation engines, using machine learning, suggest what movies or TV shows to watch next, based on user preferences. Self-driving cars that rely on machine learning to navigate may soon be available to consumers. Machine learning is a constantly developing field. For this reason, there are considerations to keep in mind when working with machine learning methods or analyzing the impact of machine learning processes. In this article, common machine learning methods of supervised and unsupervised learning and common algorithmic approaches in machine learning. including k-nearest neighbor algorithm, decision tree learning and deep learning. His findings showed. With the expansion of the scope and fields of study and the use of machine learning in different fields, depending on the changing needs and complexity of different problems, different types of machine learning algorithms have emerged, each of which has its own efficiency and applications. . So you can choose the algorithm that best suits your business needs. In some cases, experts use a combination of these algorithms because one algorithm alone may not be able to solve a particular problem.

The unemployment rate and Gross Domestic Product (GDP) are among the most important economic indicators that understanding their current and future trends can help policymakers and decision-makers adopt appropriate solutions to prevent crises and improve the country’s economic situation. Accurate prediction of these two indicators can be useful in future planning and improving the country’s economy and people’s livelihoods. In recent years, artificial intelligence techniques and tools, given their many capabilities, can play a very important role in predicting important economic indicators. Therefore, given the high importance of the two indicators of unemployment rate and GDP on the economy of our country Iran, this article intends to first predict these two indicators separately and then predict the rate of GDP growth based on the unemployment rate using artificial intelligence techniques. For this purpose, in this research, seasonal data related to GDP and its components and the unemployment rate for the years 1976-2022 have been used. Also, machine learning models based on regression have been used for prediction. The results show that the predictions of the mentioned models have an appropriate accuracy in terms of evaluation criteria such as root mean square error, mean absolute error, mean absolute percentage error, which indicates.

Consumption as the most stable variable of GDP, plays an important role in the economy. According to consumption theories, the form of the consumption function in the short and long run changes the coefficients of macro-variables multiplier, This has an effect on how macro policies affect economic variables. In this regard, in the present study, an attempt has been made to empirically test the hypothesis of the ratch effect of Duesenberry consumption using annual time series data of the Iranian economy during the period 1976-2020. For this purpose, the Autoregressive Distributed Lag (ARDL), BMA_ADL and deep learning method LSTM (Long short term memory) has been used. results indicate that this hypothesis is not consistent for Iran. In other words, empirical evidence shows that, contrary to Dusenberry's theory, the slope of the consumption function in the short term is higher than the slope of the long-run function. As a result, the Multiplier coefficient of demand management policies in the short run is more than the long run. This pattern of consumption behavior can be called the precautionary behavior of households, which originates from Iran's economic conditions.

The pension coverage of the Iranian Social Security Organization for self-employed workers is offered at three contribution rates of 12, 14 and 18 percent, but looking at the statistics shows that the demand for these types of insurances is low. This research investigates the characteristics of these insured groups by using data mining and applying two machine learning algorithms, decision tree and random forest, and predicts their behavior by providing a classification model. This will help the Social Security Organization to improve customer relationship management. For this purpose, the information of 1286174 insured persons of self-employed in 2020 was used, which includes the characteristics of age, gender, average monthly income, the years of service, and the type of self-employed pension scheme. The obtained results show that women mainly apply for the scheme with 12 percent contribution, while men tend to be covered by schemes with contribution rates of 14 and 18 percent due to the burden of supporting the family. Also, for men, the demand for schemes of 14 and 18 percent increases with the increase of age, income and years of service, but there are no such trends for women. According to the obtained results, years of service and then gender are decisive in choosing the type of pension scheme in such a way that according to the prediction of the model, people with less than 4.5 years of service are known as definite applicants for 12 percent self-employed pension scheme.

Economic uncertainty is one of the important and influential factors on economic policies and their results, and in such a situation, rational decisions are replaced by other methods. Various studies has shown the effect of economic uncertainty on inflation, investment, economic growth, consumption and demand for money.Uncertainty is difficult to measure due to its invisibility, and as the uncertainty measurement methods improve, the measurement of its effect on various economic variables and markets and the prediction of their behavior in response to the actions of economic agents will be more accurate.The main aim of this article is to measure the economic uncertainty index by using news published in social networks. This method of measurement has become very important with the widespread use of social networks.

 THEORETICAL FRAMEWORK:

Uncertainty is one of the most controversial concepts in the philosophy and methodology of economics. The history of the concept of economic uncertainty goes back to David Hume. There are three categories of theories about economic uncertainty. The first group believes that the future reality is unchangeable and predetermined and economic decision makers have perfect information. In this view, there is no such thing as uncertainty and the world is in complete certainty. 18th century the economists of were the first group to present this theory. The second group believes that the reality of the future is unchangeable and predetermined and the decision makers are able to know the future. These economists use objective conditional probability functions to solve the future uncertainty problem. The third class considers the future reality to be changeable and unknown. The starting point of these theories started from the study of the Chicago school economist Frank Knight titled "Risk, Uncertainty and Profit". He clearly distinguished between the two concepts of risk and uncertainty. Keynes also reached the same results as Knight. In general, in a situation where the economy has a high level of uncertainty, the theories of the first and second category have a good explanation. But in confronting with exogenous shocks such as the corona virus epidemic, war and financial crisis, the concept of uncertainty will be more appropriate in the theories of the third category. This study will measure this index based on fundamental uncertainty (the third category).

In this article, the economic uncertainty index in Iran was measured from January 2017 to December 2020 by monitoring and analyzing 3,117,960 news from 28 popular and influential Iranian Telegram channels. To analyze these news, we used "supervised machine learning" methods. In the first step, 13,404 news items were labeled by human evaluators according to their impact on uncertainty. The labels had two modes "affecting uncertainty" and "neutral". Then by using four algorithms ("C4.5" from decision tree methods, "Multilayer Perceptron" from artificial neural network methods, "Logistics" from function-oriented methods and "Simple Bayes" from Bayesian methods) labeling of the whole news was done. The economic uncertainty index was calculated numerically and based on the number of news items that affect economic uncertainty, the measurement and value of this index was standardized, and then the quality of the index was evaluated with historical evidence, relabeling and comparison with the index based on Google data.

Among the 4 media-based uncertainty indicators, 3 indicators can better explain the historical events of this period. Among them, the best performance is determined by C4.5 algorithm from the decision tree methods. After this algorithm, multilayer perceptron, logistic has the best performance and the weakest performance belongs to the simple Bayes method. Media-based economic uncertainty index trend with C4.5 method is consistent with the important events of the study period, in such a way that the highest level of uncertainty occurred during the period when Trump announced his withdrawal from the JCPOA until the official withdrawal of the United States from the JCPOA. In general, it can be said that the fluctuations of the economic uncertainty index have been limited and have several jumps, which are due to the withdrawal of the United States from the JCPOA, the oil embargo and the assassination of Sardar Soleimani.In the logistic algorithm, the highest level of uncertainty dates back to the end of 2020. The period that coincides with Trump's presidential election. The level of economic uncertainty increases after Trump's official withdrawal from the JCPOA and reaches its peak with oil sanctions.The output of the multilayer perceptron algorithm indicates that the average level of uncertainty has not changed significantly.  In the simple Bayes algorithm, the highest level was also reached during the period of the withdrawal of the United States from the JCPOA and the increase in enrichment.  The results of the regression showed that economic uncertainty has a positive and significant effect on the average logarithm of the exchange rate with multilayer perceptron, logistic and simple methods. This effect is larger in the multilayer perceptron model, which had better performance based on machine learning indicators.

CONCLUSIONS & SUGGESTIONS:

The calculated economic uncertainty index is consistent with the important events of the study period, such as the US withdrawal from the JCPOA, Iran’soil sanctions, and the escalation of the US confrontation with Iran in the assassination of Sardar Soleimani. It is suggested that daily calculation of this index be used to reduce uncertainty in the managing future events. We employed GARCH model to test effect of Media-based Economic Uncertainty index on Iranian exchange rate. The results showed that Economic Uncertainty index has poisitve effect on exchange rate.

Lack of liquidity management of banks is one of the most important risks for any bank and lack of attention to liquidity risk leads to irreparable consequences. Preventing liquidity risk requires a comprehensive measurement method but liquidity risk is complicated issue, and this complexity makes it difficult to provide a proper definition. In addition, defining liquidity risk determinants and formulation of the related objective function to measurement its value is a difficult task. To address these problems and assess liquidity risk and its key factors, in this study we propose a model that uses artificial neural networks and Bayesian networks. Design and implementation of this model includes several algorithms and experiments to validate the model. In this paper, we have used Levenberg-Marquardt and Genetic optimization algorithms to teach artificial neural networks. We have also implemented a case study in Bank Mellat to demonstrate the feasibility, efficiency, accuracy and flexibility of the research liquidity risk measurement model.